InStore Optimization: 8 Projects for Physical Retail

InStore Optimization projects quantify customers and staff activities. We focus on specific points in the Customer’s Journey.

Here are some examples:

InStore Purchase Points

The decision to purchase is not done in the checkout. People made the purchase decision inside the store. And we call it the “Purchase Point”.

In apparel, for example, many purchase points are done in fitting rooms. In supermarkets, purchase points occur when customers put products in shopping carts.

Staff Activities

The staff has three core activities in the store. First, they do back-of-the-store activities, from logistics to management. Second, activities on the sales floor, such as stocking products and cashiers. And third, we have customer service.

Most retailers quantify staff activities by Activity Based Costing (ABC). This is an effective accounting method to define the cost of payroll. Our focus is on measuring staff’s activities in the context of productivity. In other words, how the activity impacts the path to purchase.

10 Ideas to InStore Optimization

Path Analysis

Many people equate path analysis with the Customer’s Journey. This is not accurate. Path Analytics is about trajectory. It consists of three phases: how people reached a specific location, how long they stayed, and where they go next. The analytics provides us with “Local Traffic Patterns” or “Local Demand”.

For example, we monitor the path of a single person (below). The shopper reached the frozen coffin via the front of the back island. This is important because the person has not gone through the “custom cuts” service zone (in back of picture).

Engage Time

The analytics of engagement has two dimensions. One relates to zone activity. Here we measure optimization per display. The other depends on the tracking of an individual shopper.

In the video, we track one person across three purchase points. The first point is the fresh packages of meats and seafood. This stop garnered the longest stay time. The second point was frozen seafood on the wall. And the third point was the aisle side of the frozen coffin. Thus the Engage Time depends on trajectory, products, and individual behaviors.

The emerging technologies capturing Time in Seconds  are a game changer.

Master Projects in People Tracking

Calls to Action

The design of Calls to Action (CTA) is a major area of study for web optimization. But linear concepts do not work in the physical store.  We lacked real-time data about layouts, planograms, and packaging. This is changing to the additional layers of data from tracking technologies.

In the video, we cover few examples of Calls to Action. We see new products, discounted items, premium branding, and end cap positioning. The Call to Action is a function of discovery, engagement and conversion.

Service Time

Usually we talk about structured customer service. We measure counters and checkouts. But there is also the ad hoc customer service, which has a powerful impact on basket size and conversion.

The scene in the video reminds me of a recent visit to my local mart, Trader’s Joe. As I loitered a bit in the wine aisle, an associate came over and asked if she could help. Instead of buying no wine (my intent), I ended up with 3 bottles from unfamiliar brands. The encounter increased my conversion (purchase), basket (3 bottles), and sales (undiscounted brands).


The metric of occupancy has double meaning in behavior analytics. On one hand it describes local demand. On the other, it captures the friction from crowd behaviors. It is a useful metric to find a connection between local demand and purchase points.

Occupancy is a versatile metric. We use occupancy in store design, hot and cold zone, and crowd analytics. And Occupancy is a great metric for A/B testing of local demand.

Preventing Friction

Abandons, bottlenecks, and checkouts create obstacles to shopping. The study of friction is one of the most overlook areas of study InStore.

The most known point of friction is the checkout, where we use the technology of Queue Management. Kroger reduced their waiting time from 4 minutes to 30 seconds, and saw an increase of 1-2% in comp sales.

Bringing it All Together

InStore Behavior Analytics combines three disciplines. Location Analytics is a function of detecting and tracking people. Behavior Science brings marketing, neurology, and psychology together. And InStore Conversion adapts online growth to the physical store.

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